Training Course on Advanced Spatial Querying and SQL - PostGIS

Module 1: Introduction to PostGIS and Spatial Databases

Theory:

What is PostGIS? Role of spatial extensions in PostgreSQL.

Differences between traditional relational databases and spatial databases.

Understanding spatial data models: vector (geometry, geography) vs raster.

Spatial Reference Systems (SRID) and coordinate transformations.

Methods:

Installing and enabling PostGIS in PostgreSQL.

Loading data with shp2pgsql, ogr2ogr, and QGIS DB Manager.

Exploring metadata tables (geometry_columns, spatial_ref_sys).

Applications: Managing city infrastructure datasets, land records, and environmental monitoring.

Hands-on Exercise:

Install PostGIS.

Import shapefiles of administrative boundaries and raster DEM data.

Run basic SQL queries (ST_Area, ST_Length, ST_Centroid) to explore the dataset.

Module 2: Advanced Spatial Querying in SQL

Theory:

Spatial predicates: ST_Contains, ST_Within, ST_Touches, ST_Overlaps, ST_DWithin.

Attribute + spatial filtering for complex queries.

Writing advanced joins: spatial joins, self-joins, cross joins with spatial conditions.

Using Common Table Expressions (CTEs) to structure spatial queries.

Methods:

Querying relationships between different layers (roads vs buildings, rivers vs settlements).

Applying Boolean logic to combine spatial and non-spatial filters.

Applications:

Identifying buildings at risk within flood zones.

Finding schools within a 1 km radius of highways.

Detecting overlapping land parcels for cadastral audits.

Hands-on Exercise:

Use ST_Intersects to find schools within 500m of major roads.

Build a nested query to identify population clusters near hospitals.

Use CTEs to chain multiple spatial queries into one analysis.

Module 3: Spatial Analysis and Geoprocessing with PostGIS Functions

Theory:

Analytical functions: buffering, clipping, dissolving, union, intersection.

Proximity analysis with ST_Distance, ST_DWithin, and ST_ClosestPoint.

Raster analysis: ST_Value, ST_Clip, ST_MapAlgebra.

Combining raster and vector datasets for integrated analysis.

Methods:

Multi-criteria decision analysis with weighted overlays.

Using ST_Union and ST_Intersection for geoprocessing workflows.

Applications:

Site suitability analysis (e.g., locating new hospitals or warehouses).

Environmental impact studies (e.g., deforestation hotspots).

Urban planning (green space accessibility, zoning compliance).

Hands-on Exercise:

Perform a site suitability analysis by overlaying slope, land-use, and population density layers.

Use ST_Buffer and ST_Difference to create buffer zones for protected areas.

Run a raster-vector overlay to identify settlements on steep slopes.

Module 4: Performance Optimization and Indexing in PostGIS

Theory:

Why optimization matters for big spatial datasets.

Indexing strategies: GiST (R-tree), SP-GiST, BRIN indexes.

Partitioning spatial tables for performance.

Query execution plans and performance tuning (EXPLAIN, ANALYZE).

Methods:

Creating indexes (CREATE INDEX ON … USING GIST).

Simplifying geometries (ST_Simplify, ST_SnapToGrid).

Using bounding boxes (&&, ST_Envelope) for faster queries.

Applications:

National-scale census datasets.

Real-time routing and logistics applications.

Telecom network coverage analysis.

Hands-on Exercise:

Compare performance of spatial queries with and without indexes.

Optimize a slow-running query by restructuring it and adding indexes.

Use EXPLAIN ANALYZE to interpret query execution steps.

Module 5: Integrating PostGIS into GIS Applications and Decision-Making

Theory:

Using PostGIS as the backend for GIS systems.

Connecting PostGIS to desktop tools (QGIS, ArcGIS) and web GIS (GeoServer, MapServer).

Automating workflows with Python (psycopg2, SQLAlchemy, GeoPandas).

Building decision-support dashboards with PostGIS queries.

Methods:

Exposing spatial queries as web services (WMS, WFS).

Integrating PostGIS with business intelligence platforms (Tableau, Power BI).

Applications:

Emergency response planning using live GIS dashboards.

Transportation network optimization for logistics companies.

Agriculture DSS for yield prediction and land monitoring.

Capstone Project (Hands-on):

Design a mini GIS decision-support tool powered by PostGIS queries, such as:

A flood risk assessment dashboard.

A logistics network optimization tool.

A public health service coverage analysis.